Identify the challenges, strategies and resources for multi-omics data integration using biological examples. 

The virtual course will focus on the use of public data resources and open access tools for enabling integrated working, with an emphasis on data visualisation. Working with public domain data can provide added value to data derived through a researcher’s own work and additionally  inform experimental design. This course is highly relevant in the current research scenario, where an increased volume of data across the whole spectrum of biology has created both more opportunities and challenges to identifying novel perspectives and answering questions in the life sciences. This course will focus on issues around data integration, but will not include systems biology modelling or machine learning approaches.

A major element of this course is a group project, where participants will be organised in small groups to work together on a challenge set by trainers from EMBL-EBI data resource and research teams. These will allow participants to explore the bioinformatics tools and resources introduced in the course and to apply these to a set problem, providing hands-on experience of relevance to their own research. The group work will culminate in a presentation session involving all participants on the final day of the course, giving an opportunity for wider discussion on the benefits and challenges of integrating data.

Virtual course

The course will involve participants learning via pre-recorded lectures, live presentations, and trainer Q&A sessions. The content will be delivered over Zoom, with additional text communication over Slack.

Computational practicals will be run on EMBL-EBI's virtual training infrastructure; this means there is no need to have a powerful computer to run exercises or a requirement to install complex software before the course. Trainers will be available to provide support, answer questions, and further explain the analysis during these practicals.

Participants will need to be available between the hours of 09:30-17:30 GMT each day of the course.

Who is this course for?

This introductory course is aimed at biologists who are embarking on multi-omics projects and computational biologists / bioinformaticians who wish to gain a better knowledge of the biological challenges presented when working with integrated datasets.

Some practical sessions in the course require a basic understanding of the Unix command line and the R statistics package. If you are not already familiar with these then please ensure that you complete these free tutorials before you attend the course:

• Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix
• Basic R concept tutorials: www.r-tutor.com/r-introduction

For advanced-level training in using large-scale multi-omics data and machine learning to infer biological models you may wish to consider our course on Systems Biology: From large datasets to biological insight.

What will I learn?

Learning outcomes

After this course you should be able to: 

• Discuss motivations for working in an integrated manner 
• Comprehend the importance of data standards and the collection of metadata 
• Identify challenges for bringing different data types together 
• Use a range of bioinformatics tools to organise and visualise biological data

Course content

During this course you will learn about:

• Data standards, curation and ID mapping
• Quality control for data integration
• Analysis and visualistion: Cytoscape, InterMine, Multi-omics factor analysis (MOFA), ReactomeGSA
• Challenges and best practice for working in an integrated manner with biological data

Trainers

Please read our page on application advice before starting your application. In order to be considered for a place on this course, you must do the following:

• Complete the online application form providing answers as directed
• Ensure you add relevant information to the "Application submission" section where you are asked to provide three 100-word paragraphs that cover your:
  • scientific biography
  • work history
  • current research interests
• Upload a letter of support from your supervisor or a senior colleague detailing reasons why you should be selected for the course

Please submit all documents during the application process by midnight on Friday 08th January 2021. 

Incomplete applications will not be considered.

All applicants will be informed of the status of their application (successful, waiting list, rejected) by Friday 22nd January 2021. If you have any questions regarding the application process please contact Jane Reynolds (reynolds@ebi.ac.uk).

Participant flash talks

All participants will be asked to give a short presentation about their research work as part of the course. These provide an opportunity to share their research with the other participants and provide a forum for discussion. Further details will be provided following registration